Facial expression recognition method based on convolutional neural network

Abstract

The invention discloses a facial expression recognition method based on a convolutional neural network. The method includes facial expression image data set preprocessing, construction of an improvedconvolutional neural network, weight optimization and training, and classification processing of facial expressions. The method introduces continuous convolution into a conventional convolutional neural network to obtain the improved convolutional neural network, the improved convolutional neural network adopts a small-scale convolution kernel to perform feature extraction, so that extracted facial expression features are more precise, two continuous convolution layers also enhance a nonlinear expression capability of the network, in addition, the convolutional neural network and an SOM neuralnetwork are cascaded to form a pretraining network to perform pre-learning, neurons with an optimal learning result are used for initializing the improved convolutional neural network, and the methodprovided by the invention can effectively improve facial expression image recognition precision.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a method for recognizing facial expressions based on a convolutional neural network. Background technique [0002] Facial expression recognition technology refers to extracting facial expression features from a given facial expression image and assigning them to a specific type of facial expression. The research on facial expression recognition has a wide range of application values. Fast facial expression recognition helps to analyze the emotions of the recognized objects, and can realize emotional communication between machines and humans in the field of intelligent machines. Facial expression recognition can also be applied to the field of Internet interest capture. Facial expression recognition is a prerequisite for computers to understand human emotions. Efficient and accurate facial expression recognition is conducive to computers recommending music, movies and sw...

AI Technical Summary

Problems solved by technology

Traditional feature extraction and construction methods include active appearance model, Gabor wavelet transform, local binary model, etc. The same point of these methods is that they use artificially set features and use human experience for feature extraction, which may easily lead to partial expression features The loss of information, to a certain extent, loses the original feature information, which makes the recognition performance obtained unsatisfactory. In addition, the extracted feature dimensions are very large, which is not conducive to the classification of the next stage.

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